{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_SEQUENCE_LENGTH = 200 # 句子 上限200个词\n",
    "EMBEDDING_DIM = 100 # 100d 词向量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good len: 1294531\n",
      "bad len: 48126\n"
     ]
    }
   ],
   "source": [
    "good = []\n",
    "bad = []\n",
    "for line in open('data/goodqueries.txt'):\n",
    "    good.append(line.strip('\\n'))\n",
    "for line in open('data/badqueries.txt'):\n",
    "    bad.append(line.strip('\\n'))\n",
    "print('good len:', len(good))\n",
    "print('bad len:', len(bad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: 192504\n",
      "/103886/ <svg onload=location='//p0.al'>\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "labels = []\n",
    "\n",
    "length = len(bad)\n",
    "scale = 3\n",
    "data.extend(good[:length * scale]) # 只取部分数据\n",
    "data.extend(bad)\n",
    "labels.extend([1] * length * scale)\n",
    "labels.extend([0] * length)\n",
    "print('data:', len(data))\n",
    "print(data[0], data[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 175 unique tokens.\n",
      "Shape of data tensor: (192504, 200)\n"
     ]
    }
   ],
   "source": [
    "# https://keras-cn-docs.readthedocs.io/zh_CN/latest/blog/word_embedding/\n",
    "import numpy as np\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "# tokenizer\n",
    "texts = data\n",
    "tokenizer = Tokenizer(char_level=True) # 字向量\n",
    "tokenizer.fit_on_texts(texts)\n",
    "word_index = tokenizer.word_index\n",
    "\n",
    "# sequences\n",
    "sequences = tokenizer.texts_to_sequences(data)\n",
    "\n",
    "# padding\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "print('Shape of data tensor:', data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "token_path = 'model/tokenizer.pkl'\n",
    "pickle.dump(tokenizer, open(token_path, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "# 打乱顺序\n",
    "index = [i for i in range(len(data))]\n",
    "random.shuffle(index)\n",
    "data = np.array(data)[index]\n",
    "labels = np.array(labels)[index]\n",
    "\n",
    "TRAIN_SPLIT = 0.8 # 20% 测试集\n",
    "TRAIN_SIZE = int(len(data) * TRAIN_SPLIT)\n",
    "\n",
    "X_train, X_test = data[0:TRAIN_SIZE], data[TRAIN_SIZE:]\n",
    "Y_train, Y_test = labels[0:TRAIN_SIZE], labels[TRAIN_SIZE:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train len: 154003\n",
      "test len: 38501\n"
     ]
    }
   ],
   "source": [
    "print('train len:', len(X_train))\n",
    "print('test len:', len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
    "\n",
    "G = 1 # GPU 数量\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)\n",
    "session = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True))\n",
    "K.set_session(session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bi-LSTM + CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 200, 100)          17600     \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 200, 128)          84480     \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 198, 128)          49280     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 198, 128)          512       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 198, 128)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 49, 128)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                401472    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 1)                 4         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 553,669\n",
      "Trainable params: 553,283\n",
      "Non-trainable params: 386\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, BatchNormalization, Flatten\n",
    "from keras.layers import Dense, LSTM, Convolution1D, MaxPooling1D\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.layers.wrappers import Bidirectional\n",
    "\n",
    "QA_EMBED_SIZE = 64\n",
    "DROPOUT_RATE = 0.3\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Embedding(len(word_index) + 1, EMBEDDING_DIM, input_length=MAX_SEQUENCE_LENGTH))\n",
    "model.add(Bidirectional(LSTM(QA_EMBED_SIZE, return_sequences=True, dropout=DROPOUT_RATE, recurrent_dropout=DROPOUT_RATE)))\n",
    "model.add(Convolution1D(filters=128, kernel_size=3, padding='valid', activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling1D(4))\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(QA_EMBED_SIZE))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(1))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型可视化 https://keras-cn.readthedocs.io/en/latest/other/visualization/\n",
    "from keras.utils import plot_model\n",
    "from IPython import display\n",
    "\n",
    "# pip install pydot-ng\n",
    "# sudo apt-get install graphviz\n",
    "plot_model(model, to_file=\"img/model-blstm-cnn.png\", show_shapes=True)\n",
    "display.Image('img/model-blstm-cnn.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 107802 samples, validate on 46201 samples\n",
      "Epoch 1/3\n",
      "107802/107802 [==============================] - 1230s 11ms/step - loss: 0.2063 - acc: 0.9795 - precision: 0.9861 - recall: 0.9867 - f1: 0.9862 - val_loss: 0.0828 - val_acc: 0.9960 - val_precision: 0.9955 - val_recall: 0.9992 - val_f1: 0.9973\n",
      "Epoch 2/3\n",
      "107802/107802 [==============================] - 1164s 11ms/step - loss: 0.0593 - acc: 0.9960 - precision: 0.9961 - recall: 0.9986 - f1: 0.9973 - val_loss: 0.0375 - val_acc: 0.9974 - val_precision: 0.9970 - val_recall: 0.9996 - val_f1: 0.9983\n",
      "Epoch 3/3\n",
      "107802/107802 [==============================] - 1161s 11ms/step - loss: 0.0275 - acc: 0.9976 - precision: 0.9975 - recall: 0.9992 - f1: 0.9984 - val_loss: 0.0210 - val_acc: 0.9973 - val_precision: 0.9967 - val_recall: 0.9996 - val_f1: 0.9982\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc41005fef0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n",
    "# from keras.utils import multi_gpu_model\n",
    "from evaluate import *\n",
    "\n",
    "EPOCHS = 3\n",
    "BATCH_SIZE = 64 * G\n",
    "VALIDATION_SPLIT = 0.3 # 30% 验证集\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "model_checkpoint = ModelCheckpoint('model/model-blstm-cnn.h5', save_best_only=True, save_weights_only=True)\n",
    "tensor_board = TensorBoard('log/tflog-blstm-cnn', write_graph=True, write_images=True)\n",
    "\n",
    "# model = multi_gpu_model(model)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "                  optimizer='adam',\n",
    "                  metrics=['accuracy', precision, recall, f1])\n",
    "\n",
    "model.fit(X_train, Y_train, epochs=EPOCHS, batch_size=BATCH_SIZE, \n",
    "          validation_split=VALIDATION_SPLIT, shuffle=True, \n",
    "          callbacks=[early_stopping, model_checkpoint, tensor_board])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38501/38501 [==============================] - 91s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.018343773450079207,\n",
       " 0.9978442118386536,\n",
       " 0.9973908721098685,\n",
       " 0.999754650097835,\n",
       " 0.9985566066768596]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test, verbose=1, batch_size=BATCH_SIZE)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
